Cost-Sensitive YOLOv5 for Detecting Surface Defects of Industrial Products
Owing to the remarkable development of deep learning algorithms, defect detection techniques based on deep neural networks have been extensively applied in industrial production. Most existing surface defect detection models assign equal costs to the classification errors among different defect cate...
Main Authors: | Ben Liu, Feng Gao, Yan Li |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-02-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/5/2610 |
Similar Items
-
YOLOv5-Sewer: Lightweight Sewer Defect Detection Model
by: Xingliang Zhao, et al.
Published: (2024-02-01) -
Application of Improved YOLOv5 Algorithm in Lightweight Transmission Line Small Target Defect Detection
by: Zhilong Yu, et al.
Published: (2024-01-01) -
Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5
by: Yi Qu, et al.
Published: (2023-10-01) -
Surface Defect Detection Method of Wooden Spoon Based on Improved YOLOv5 Algorithm
by: Siqing Tian, et al.
Published: (2023-11-01) -
Surface defect detection of steel based on improved YOLOv5 algorithm
by: Yiwen Jiang
Published: (2023-11-01)